Before we give an outline of the paper, we briefly review random survival forests, an important component of the proposed method. A random survival forest is a nonparametric survival regression estimator that provides weighted Kaplan-Meier curves for each point in the feature space with weights given by adaptive kernel estimates. In general, a random forest is an ensemble of trees each of which are generated by recursively partitioning the data into two daughter nodes. In the [1]} Classification and Regression Trees, or CART, approach, partitions are made by searching across all variables and data midpoints, and the best cut point that maximizes the heterogeneity between the resulting daughter nodes based on the mean differences is taken. In [2]} random forests, [1]} trees are built and averaged based on multiple bootstrap samples considering only a random subset of the variables when splitting each node. This randomization—bootstrapping and random partitioning—brings diversity among trees, and thus the variability of the estimators can be less than that obtained in the CART approach. [4]} added an alternative randomization mode by restricting the candidate threshold values to a randomly chosen cut point for each variable.
